Study area Data used Methods

HYPERSPECTRAL DATA FOR LAND USE LAND COVER CLASSIFICATION Divya Vijayan V a, , G Ravi Shankar , T Ravi shankar a National Remote Sensing Centre, Balanagar, Hyderabad, 500037 divya_v, ravishankar_g, ravishankar_tnrsc.gov.in KEY WORDS: Hyperion, Spectral Angle Mapper, Red Edge Inflection Point ABSTRACT: An attempt has been made to compare the multispectral Resourcesat-2 LISS III and Hyperion image for the selected area at sub class level classes of major land use land cover. On-screen interpretation of LISS III resolution 23.5 m was compared with Spectral Angle Mapping SAM classification of Hyperion resolution 30m. Results of the preliminary interpretation of both images showed that features like fallow, built up and wasteland classes in Hyperion image are clearer than LISS-III and Hyperion is comparable with any high resolution data. Even canopy types of vegetation classes, aquatic vegetation and aquatic systems are distinct in Hyperion data. Accuracy assessment of SAM classification of Hyperion compared with the common classification systems followed for LISS III there was no much significant difference between the two. However, more number of vegetation classes could be classified in SAM. There is a misinterpretation of built up and fallow classes in SAM. The advantages of Hyperion over visual interpretation are the differentiation of the type of crop canopy and also crop stage could be confirmed with the spectral signature. The Red edge phenomenon was found for different canopy type of the study area and it clearly differentiated the stage of vegetation, which was verified with high resolution image. Hyperion image for a specific area is on par with high resolution data along with LISS III data. Corresponding author at: National Remote Sensing Centre. Hyderabad Tel: 04023884278 E-mail: diva_vijyahoo.com.

1. INTRODUCTION

Hyperspectral remote sensing are characterised by imaging and spectroscopic property, which differentiates the terrestrial features into unique spectral signature. This property is valuable in evidently classifying land use cover features especially vegetation and water bodies. A major limitation of broadband remote sensing products is that they use average spectral information over broadband widths resulting in loss of critical information available in specific narrow bands. Thus the advent of hyper spectral remote sensing with continuous narrow band information opens the possibility of identifying even the species level discrimination in vegetation studies. Recent developments in hyperspectral remote sensing or imaging spectrometry have provided additional bands within the visible, near infrared NIR and shortwave infrared SWIR region of the electromagnetic spectrum. Most hyperspectral sensors acquire radiance information in less than 10 nm bandwidths from the visible to the SWIR 400-2500 nm. Hyper spectral remote sensing by virtue of its contiguity and narrow bandwidth is increasingly used to characterize, model, classify, and map agricultural crops and natural vegetation. Schmidt and Skidmore, 2002 attempted hyperspectral studies at the herbaceous and grassland level and showed that 27 saltmarsh vegetations could be discriminated. Hyper spectral applications for vegetation studies Schlerf, 2011 introduced the red edge phenomenon and red edge inflection point REIP, which is correlated to the chlorophyll content in the canopy. Hyperspectral remote sensing in vegetation studies include species composition, vegetation or crop type biophysical properties, biochemical properties disease and stress studies, nutrients, moisture, light use efficiency and net primary productivity Thenkabail, 2012. Hyperion hyperspectral imagery over a given region, when combined with either SVMs or ANNs to classifiers, can potentially enable a wider approach in land usecover mapping Petropoulos et al., 2012. An attempt has been made to compare the multispectral LISS III and Hyperion image at sub class level classification of major land use land cover features to understand the potential use of hyperspectral data in Land use study

2. MATERIALS AND METHOD

2.1 Study area

The Study area covers a strip of hyperion image with 21 3912.81 to 23 31 49 N latitude and 72 42 05.50 and 73 07 04.51E longitude and covers area of Anand and Valsad in Gujarat in western India. Figure 1. Location of study area ISPRS Technical Commission VIII Symposium, 09 – 12 December 2014, Hyderabad, India This contribution has been peer-reviewed. doi:10.5194isprsarchives-XL-8-991-2014 991

2.2 Data used

Hyperion data of EO-1 used in the present study was acquired over out test site on March 16th, 2013 from the United States Geological Survey USGS archive. The imagery was received as a full long scene 185-km strip and at level 1 L1GST processing level in GeoTIFF format, stored in 16-bit signed integer radiance values. Hyperion acquired over 400-2500 nm in 220 narrow-bands each of 10-nm wide bands. Of these there are 196 bands that are calibrated. Which includes bands 8 427.55 nm to 57 925.85 nm in the visible and near-infrared and bands 79 932.72 nm to band 224 2395.53 nm in the short wave infrared. Hyperion bands in noise region were dropped and 153 useful bands remained for the study. For comparative evaluation, multispectral Resourcesat 2 LISS III data with a spatial resolution of 23m has been used. Also world view data acquired on February 13 th 2013 has been used to supplement and improve the classification.

2.3 Methods

The L1G of hyperion data product is radiometrically corrected, geometrically resampled, and registered to a geographic map projection with elevation correction applied. The data 16-bit and format HDF Hierarchical Data Format and converted toBand Interleaved by Pixel BIP or Band Interleaved by Line BIL The image was subset to remove uncalibrated bands and the bad lines were removed. Area of interest was extracted from the subset image Atmospheric correction for the data has been done using FLAASH Fast Atmospheric Analyst Line of the Spectral Hypercube - For atmospheric correction, where the radiance will be converted to reflectance. The parameters adopted for the implementation of atmospheric correction were: sensor altitude: 705 km above sea level. mean elevation of the image area 0.6 km kilometers above the sea, pixel size 30m; atmospheric model Mid-Latitude Summeraerosol model - rural scattering algorithm MODTRAN – ISAACS Spectral and spatial using genetic algorithm and geometry diffusion respectively using minimum noise fraction has been used for dimensionality reduction. Spectral Angle Mapper SAM was used for comparing the angles between the reflectance spectrum of the classified and the reference spectrum obtained from spectral library generated for different features. Each pixel is assigned to a class according the lowest spectral angle value. The results are finally validated with the available high resolution data.

3. RESULTS AND DISCUSSION